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f173e69187
* introduce dockerfiles. * ensure golden tests are run not just collected. * Skip CI download of dockerfiles. * add StarCraft environment and tests. * add minimaps starcraft validation parameters. * Add functional test running (from Ayoob) * pin mujoco_py version to a 1.5 compatible release. * fix config syntax issue. * pin remaining mujoco_py install calls. * Relax pin of gym version in gym Dockerfile. * update makefile based on functional test filtering.
69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
from collections import OrderedDict
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from rl_coach.agents.ddqn_agent import DDQNAgentParameters
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from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
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from rl_coach.architectures.head_parameters import DuelingQHeadParameters
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.starcraft2_environment import StarCraft2EnvironmentParameters
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from rl_coach.filters.action.box_discretization import BoxDiscretization
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from rl_coach.filters.filter import OutputFilter
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.memories.memory import MemoryGranularity
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from rl_coach.schedules import LinearSchedule
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####################
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# Graph Scheduling #
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####################
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = TrainingSteps(10000000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(50)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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schedule_params.heatup_steps = EnvironmentSteps(50000)
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#########
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# Agent #
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#########
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agent_params = DDQNAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.0001
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agent_params.network_wrappers['main'].input_embedders_parameters = {
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"screen": InputEmbedderParameters(input_rescaling={'image': 3.0})
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}
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agent_params.network_wrappers['main'].heads_parameters = [DuelingQHeadParameters()]
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agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
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# slave_agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
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agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.1, 1000000)
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
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agent_params.output_filter = \
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OutputFilter(
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action_filters=OrderedDict([
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('discretization', BoxDiscretization(num_bins_per_dimension=4, force_int_bins=True))
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]),
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is_a_reference_filter=False
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)
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###############
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# Environment #
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###############
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env_params = StarCraft2EnvironmentParameters(level='CollectMineralShards')
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env_params.feature_screen_maps_to_use = [5]
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env_params.feature_minimap_maps_to_use = [5]
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########
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# Test #
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########
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preset_validation_params = PresetValidationParameters()
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preset_validation_params.test = True
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preset_validation_params.min_reward_threshold = 50
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preset_validation_params.max_episodes_to_achieve_reward = 200
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preset_validation_params.num_workers = 1
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graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
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schedule_params=schedule_params, vis_params=VisualizationParameters(),
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preset_validation_params=preset_validation_params)
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